Design Systems for Scalable Cloud-Native Analytics Platforms
summary
Argues for a seven-layer design stack that solves scale and governance: foundation of design tokens that include visualization palettes, density modes and semantic tokens; UI components built from tokens; data-aware components like a virtualized table that handles millions of rows with streaming, pagination and failure states; visualization primitives that encode aggregation and sampling strategies and accessibility; a semantic metric layer as single source of truth; workflow patterns for drill-downs, cross-filtering and explore→dashboard; and governance with ownership and versioning plus RBAC and contribution workflows, adopted incrementally and measured by reuse, consistency, and time-to-dashboard.
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chapters (13)
- 0:00Why Analytics Systems Break
- 2:31The Three Classic Layers
- 3:47Where It Falls Apart
- 5:37Introducing Seven Layers
- 6:15Layer One Tokens
- 7:33Layer Two UI Components
- 8:26Layer Three Data Components
- 9:37Layer Four Viz Primitives
- 11:02Layer Five Semantics
- 12:12Layer Six Workflows
- 13:33Layer Seven Governance
- 14:50Pyramid and Adoption Plan
- 17:02Key Takeaways and Wrap
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